Diffusion imaging in Parkinson&#39;s disease and Parkinsonism

ABSTRACT

A treatment efficacy of a treatment for treating a parkinsonian disease is determined. A first set of imaging information/data associated with a dMRI scan of an individual&#39;s brain captured at a first time and a second set of imaging information/data associated with a dMRI scan of the individual&#39;s brain captured at a second time are received. The individual underwent the treatment for a time period comprising at least part of the time between the first and second times. An expected change between the first and second times is determined based on a natural progression of the parkinsonian disease. The first and second sets of imaging information/data are analyzed to determine a first free-water pattern and a second free-water pattern. Based on the first and second free-water patterns, a disease progression score is determined. Based on the disease progression score and the expected change, a treatment efficacy for treating the individual with the treatment is determined.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.16/500,185, filed Oct. 2, 2019, which is a National Stage Application,filed under 35 U.S.C. § 371, of International Application No.PCT/US2018/023263, filed Mar. 20, 2018, which claims priority to U.S.Application No. 62/486,580, filed Apr. 18, 2017; the contents of whichare hereby incorporated by reference in their entireties.

FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

This invention was made with United States Government support under R01NS075012 & R01 NS052318 awarded by the National Institutes of Health(NIH). The United States Government has certain rights in the invention.

BACKGROUND

Parkinson's disease is a progressive disorder of the nervous system thataffects movement. It develops gradually, sometimes starting with abarely noticeable tremor in just one hand. Parkinson's is oftendiagnosed based on movement and/or motor symptoms, such as a tremor.However, the disease may begin prior to the onset of movement and/ormotor symptoms and it can often be difficult to distinguish betweenforms of Parkinsonism. Treatment of the disease may be more effective iftreatment were to begin in the earliest stages of the disease.Additionally, understanding of the earliest stages and the progressionof the disease require identification of individuals with the disease orat an increased risk of developing the disease prior to the onset ofnoticeable movement and/or motor symptoms. Additionally, to developmedications and/or other therapeutic treatments that effectively treatthe disease, a technique for measuring disease progression is required.Effective measurements of disease progression will facilitate futureclinical trials testing therapies for Parkinson's disease andParkinsonism.

BRIEF SUMMARY OF SOME EXAMPLE EMBODIMENTS

An example embodiment of the present invention provides one or moreprogression biomarkers of Parkinson's disease and/or other parkinsoniandiseases. For example, in an example embodiment, a progression score foran individual may be determined over a period of time based on one ormore biomarkers. In an example embodiment, biomarkers and tools fordetermining which people are at a greater risk for developingParkinson's disease and/or other parkinsonian diseases and/or earlydiagnosis of Parkinson's and/or other parkinsonian diseases areprovided. In particular, in various embodiments, these biomarkers maycomprise and/or relate to the free-water fraction in one or more areasof an individual's brain. In an example embodiment, the free-waterfraction in one or more areas of an individual's brain may be determinedbased on analysis of one or more instances of imaging information/datacorresponding to a diffusion magnetic radiation imaging (dMRI) scan ofthe individual's brain. In an example embodiment, the one or moreinstances of imaging information/data corresponding to the dMRI scan ofthe individual's brain are analyzed using a bi-tensor analysis model andpipeline for normalization and quantifying images.

According to one aspect of the present invention, a method fordetermining a free-water pattern of an individual is provided. In anexample embodiment, the method comprises receiving at a computing entityan instance of imaging information/data associated with a dMRI scan ofan individual's brain; and using by the computing entity at least one of(a) a free-water pattern template or (b) 2D or 3D threshold requirementsto determine a free-water pattern for one or more areas of theindividual's brain based on at least a portion of the instance ofimaging information/data. The free-water pattern may be used todetermine the individual's parkinsonian state.

According to another aspect of the present invention, a method fordetermining a treatment efficacy score for treating a parkinsoniandisease in an individual is provided. In an example embodiment, themethod comprises receiving at a computing entity a first set of imaginginformation/data associated with a dMRI scan of an individual's brainand associated with a first time; and receiving at the computing entitya second set of imaging information/data associated with a dMRI scan ofthe individual's brain and associated with a second time. The individualunderwent the treatment for a time period, the time period comprising atleast a portion of the time between the first time and the second time.The method further comprises determining by the computing entity anexpected change between the first time and the second time based on anatural progression of the parkinsonian disease; analyzing by thecomputing entity at least a portion of the first set of imaginginformation/data to determine a first free-water pattern; analyzing bythe computing entity at least a portion of the second set of imaginginformation/data to determine a second free-water pattern; based on thefirst free-water pattern and the second free-water pattern, determiningby the computing entity a disease progression score; and based on thedisease progression score and the expected change between the first timeand the second time, determining a treatment efficacy score for treatingthe individual with the treatment. In an example embodiment, a treatmentplan for the individual is determined based on the treatment efficacyscore.

According to still another aspect of the present invention, an apparatusfor determining a free-water pattern of an individual is provided. In anexample embodiment, the apparatus comprises at least one processor, acommunications interface configured for communicating via at least onenetwork, and at least one memory storing computer program code. The atleast one memory and the computer program code are configured to, withthe processor, cause the apparatus to at least receive an instance ofimaging information/data associated with a dMRI scan of an individual'sbrain; and use at least one of (a) a free-water pattern template or (b)2D or 3D threshold requirements to determine a free-water pattern forone or more areas of the individual's brain based on at least a portionof the instance of imaging information/data. The free-water pattern maybe used to determine the individual's parkinsonian state.

According to yet another aspect of the present invention, an apparatusfor determining a treatment efficacy score for treating a parkinsoniandisease in an individual is provided. In an example embodiment, theapparatus comprises at least one processor, a communications interfaceconfigured for communicating via at least one network, and at least onememory storing computer program code. The at least one memory and thecomputer program code are configured to, with the processor, cause theapparatus to at least receive a first set of imaging information/dataassociated with a dMRI scan of an individual's brain and associated witha first time; and receive a second set of imaging information/dataassociated with a dMRI scan of the individual's brain and associatedwith a second time. The individual underwent the treatment for a timeperiod, the time period comprising at least a portion of the timebetween the first time and the second time. The at least one memory andthe computer program code are further configured to, with the processor,cause the apparatus to at least determine an expected change between thefirst time and the second time based on a natural progression of theparkinsonian disease; analyze at least a portion of the first set ofimaging information/data to determine a first free-water pattern;analyze at least a portion of the second set of imaging information/datato determine a second free-water pattern; based on the first free-waterpattern and the second free-water pattern, determine a diseaseprogression score; and based on the disease progression score and theexpected change between the first time and the second time, determine atreatment efficacy for treating the individual with the treatment. In anexample embodiment, a treatment plan for the individual is determinedbased on the treatment efficacy score.

According to another aspect of the present invention, a computer programproduct for determining a free-water pattern of an individual isprovided. In an example embodiment, the computer program productcomprises at least one non-transitory computer-readable storage mediumhaving computer-readable program code portions stored therein. Thecomputer-readable program code portions comprise executable portionsconfigured, when executed by a processor of an apparatus, to cause theapparatus to receive an instance of imaging information/data associatedwith a dMRI scan of an individual's brain; and use at least one of (a) afree-water pattern template or (b) 2D or 3D threshold requirements todetermine a free-water pattern for one or more areas of the individual'sbrain based on at least a portion of the instance of imaginginformation/data The free-water pattern may be used to determine theindividual's parkinsonian state.

According to yet another aspect of the present invention, a computerprogram product for a treatment efficacy score for treating aparkinsonian disease in an individual is provided. In an exampleembodiment, the computer program product comprises at least onenon-transitory computer-readable storage medium having computer-readableprogram code portions stored therein. The computer-readable program codeportions comprise executable portions are configured, when executed by aprocessor of an apparatus, to cause the apparatus to receive a first setof imaging information/data associated with a dMRI scan of anindividual's brain and associated with a first time; and receive asecond set of imaging information/data associated with a dMRI scan ofthe individual's brain and associated with a second time. The individualunderwent a treatment for a time period, the time period comprises atleast a portion of the time between the first time and the second time.In an example embodiment, the computer-readable program code portionsfurther comprise executable portions are configured, when executed by aprocessor of an apparatus, to cause the apparatus to determine anexpected change between the first time and the second time based on anatural progression of the parkinsonian disease; analyze at least aportion of the first set of imaging information/data to determine afirst free-water pattern; analyze at least a portion of the second setof imaging information/data to determine a second free-water pattern;based on the first free-water pattern and the second free-water pattern,determine a disease progression score; and based on the diseaseprogression score and the expected change between the first time and thesecond time, determine a treatment efficacy for treating the individualwith the treatment.

BRIEF DESCRIPTION OF THE FIGURES

Having thus described the invention in general terms, reference will nowbe made to the accompanying drawings, which are not necessarily drawn toscale, and wherein:

FIG. 1 is an overview of a system that can be used to practiceembodiments of the present invention;

FIG. 2 is an exemplary schematic diagram of an assessment computingentity according to one embodiment of the present invention;

FIG. 3 is an exemplary schematic diagram of a user computing entityaccording to one embodiment of the present invention;

FIG. 4 provides a flowchart illustrating example processes, procedures,and/or operations for determining an individual's parkinsonian state;and

FIG. 5 provides a flowchart illustrating example processes, procedures,and/or operations for treating an individual having a parkinsoniandisease.

DETAILED DESCRIPTION OF VARIOUS EMBODIMENTS

Various embodiments of the present invention now will be described morefully hereinafter with reference to the accompanying drawings, in whichsome, but not all embodiments of the inventions are shown. Indeed, theseinventions may be embodied in many different forms and should not beconstrued as limited to the embodiments set forth herein; rather, theseembodiments are provided so that this disclosure will satisfy applicablelegal requirements. The term “or” is used herein in both the alternativeand conjunctive sense, unless otherwise indicated. The terms“illustrative” and “exemplary” are used to be examples with noindication of quality level. Like numbers refer to like elementsthroughout.

I. COMPUTER PROGRAM PRODUCTS, METHODS, AND COMPUTING ENTITIES

Embodiments of the present invention may be implemented in various ways,including as computer program products that comprise articles ofmanufacture. A computer program product may include a non-transitorycomputer-readable storage medium storing applications, programs, programmodules, scripts, source code, program code, object code, byte code,compiled code, interpreted code, machine code, executable instructions,and/or the like (also referred to herein as executable instructions,instructions for execution, computer program products, program code,and/or similar terms used herein interchangeably). Such non-transitorycomputer-readable storage media include all computer-readable media(including volatile and non-volatile media).

In one embodiment, a non-volatile computer-readable storage medium mayinclude a floppy disk, flexible disk, hard disk, solid-state storage(SSS) (e.g., a solid state drive (SSD), solid state card (SSC), solidstate module (SSM), enterprise flash drive, magnetic tape, or any othernon-transitory magnetic medium, and/or the like. A non-volatilecomputer-readable storage medium may also include a punch card, papertape, optical mark sheet (or any other physical medium with patterns ofholes or other optically recognizable indicia), compact disc read onlymemory (CD-ROM), compact disc-rewritable (CD-RW), digital versatile disc(DVD), Blu-ray disc (BD), any other non-transitory optical medium,and/or the like. Such a non-volatile computer-readable storage mediummay also include read-only memory (ROM), programmable read-only memory(PROM), erasable programmable read-only memory (EPROM), electricallyerasable programmable read-only memory (EEPROM), flash memory (e.g.,Serial, NAND, NOR, and/or the like), multimedia memory cards (MMC),secure digital (SD) memory cards, SmartMedia cards, CompactFlash (CF)cards, Memory Sticks, and/or the like. Further, a non-volatilecomputer-readable storage medium may also include conductive-bridgingrandom access memory (CBRAM), phase-change random access memory (PRAM),ferroelectric random-access memory (FeRAM), non-volatile random-accessmemory (NVRAM), magnetoresistive random-access memory (MRAM), resistiverandom-access memory (RRAM), Silicon-Oxide-Nitride-Oxide-Silicon memory(SONOS), floating junction gate random access memory (FJG RAM),Millipede memory, racetrack memory, and/or the like.

In one embodiment, a volatile computer-readable storage medium mayinclude random access memory (RAM), dynamic random access memory (DRAM),static random access memory (SRAM), fast page mode dynamic random accessmemory (FPM DRAM), extended data-out dynamic random access memory (EDODRAM), synchronous dynamic random access memory (SDRAM), double datarate synchronous dynamic random access memory (DDR SDRAM), double datarate type two synchronous dynamic random access memory (DDR2 SDRAM),double data rate type three synchronous dynamic random access memory(DDR3 SDRAM), Rambus dynamic random access memory (RDRAM), TwinTransistor RAM (TTRAM), Thyristor RAM (T-RAM), Zero-capacitor (Z-RAM),Rambus in-line memory module (RIMM), dual in-line memory module (DIMM),single in-line memory module (SIMM), video random access memory (VRAM),cache memory (including various levels), flash memory, register memory,and/or the like. It will be appreciated that where embodiments aredescribed to use a computer-readable storage medium, other types ofcomputer-readable storage media may be substituted for or used inaddition to the computer-readable storage media described above.

As should be appreciated, various embodiments of the present inventionmay also be implemented as methods, apparatus, systems, computingdevices, computing entities, and/or the like. As such, embodiments ofthe present invention may take the form of an apparatus, system,computing device, computing entity, and/or the like executinginstructions stored on a computer-readable storage medium to performcertain steps or operations. Thus, embodiments of the present inventionmay also take the form of an entirely hardware embodiment, an entirelycomputer program product embodiment, and/or an embodiment that comprisescombination of computer program products and hardware performing certainsteps or operations.

Embodiments of the present invention are described below with referenceto block diagrams and flowchart illustrations. Thus, it should beunderstood that each block of the block diagrams and flowchartillustrations may be implemented in the form of a computer programproduct, an entirely hardware embodiment, a combination of hardware andcomputer program products, and/or apparatus, systems, computing devices,computing entities, and/or the like carrying out instructions,operations, steps, and similar words used interchangeably (e.g., theexecutable instructions, instructions for execution, program code,and/or the like) on a computer-readable storage medium for execution.For example, retrieval, loading, and execution of code may be performedsequentially such that one instruction is retrieved, loaded, andexecuted at a time. In some exemplary embodiments, retrieval, loading,and/or execution may be performed in parallel such that multipleinstructions are retrieved, loaded, and/or executed together. Thus, suchembodiments can produce specifically-configured machines performing thesteps or operations specified in the block diagrams and flowchartillustrations. Accordingly, the block diagrams and flowchartillustrations support various combinations of embodiments for performingthe specified instructions, operations, or steps.

II. EXEMPLARY SYSTEM ARCHITECTURE

FIG. 1 provides an illustration of an exemplary embodiment of thepresent invention. As shown in FIG. 1, this particular embodiment mayinclude one or more imaging machines 115, one or more assessmentcomputing entities 100, one or more networks 105, and one or more usercomputing entities 110. Each of these components, entities, devices,systems, and similar words used herein interchangeably may be in director indirect communication with, for example, one another over the sameor different wired or wireless networks. Additionally, while FIG. 1illustrates the various system entities as separate, standaloneentities, the various embodiments are not limited to this particulararchitecture.

1. Exemplary Assessment Computing Entity

FIG. 2 provides a schematic of an assessment computing entity 100according to one embodiment of the present invention. An assessmentcomputing entity 100 may belong to, a medical facility, hospital,clinic, diagnostic service, healthcare provider, healthcare providergroup, and/or the like. However, the assessment computing entity 100 maybelong a third party computing service that performs remote computationsfor a medical facility. In an example embodiment, an assessmentcomputing entity 100 may be configured to control one or more imagingmachines 115 and/or to receive imaging information/data from one or moreimaging machines and/or a controller computing entity thereof.

In general, the terms computing entity, computer, entity, device,system, and/or similar words used herein interchangeably may refer to,for example, one or more computers, computing entities, desktopcomputers, mobile phones, tablets, phablets, notebooks, laptops,distributed systems, input terminals, servers or server networks,blades, gateways, switches, processing devices, processing entities,set-top boxes, relays, routers, network access points, base stations,the like, and/or any combination of devices or entities adapted toperform the functions, operations, and/or processes described herein.Such functions, operations, and/or processes may include, for example,transmitting, receiving, operating on, processing, displaying, storing,determining, creating/generating, monitoring, evaluating, comparing,and/or similar terms used herein interchangeably. In one embodiment,these functions, operations, and/or processes can be performed on data,content, information, and/or similar terms used herein interchangeably.

As indicated, in one embodiment, the assessment computing entity 100 mayalso include one or more communications interfaces 220 for communicatingwith various computing entities, such as by communicating data, content,information, and/or similar terms used herein interchangeably that canbe transmitted, received, operated on, processed, displayed, stored,and/or the like. For instance, the assessment computing entity 100 maycommunicate with user computing entities 110 and/or a variety of othercomputing entities.

As shown in FIG. 2, in one embodiment, the assessment computing entity100 may include or be in communication with one or more processingelements 205 (also referred to as processors, processing circuitry,and/or similar terms used herein interchangeably) that communicate withother elements within the assessment computing entity 100 via a bus, forexample. As will be understood, the processing element 205 may beembodied in a number of different ways. For example, the processingelement 205 may be embodied as one or more complex programmable logicdevices (CPLDs), microprocessors, multi-core processors, coprocessingentities, application-specific instruction-set processors (ASIPs),microcontrollers, and/or controllers. Further, the processing element205 may be embodied as one or more other processing devices orcircuitry. The term circuitry may refer to an entirely hardwareembodiment or a combination of hardware and computer program products.Thus, the processing element 205 may be embodied as integrated circuits,application specific integrated circuits (ASICs), field programmablegate arrays (FPGAs), programmable logic arrays (PLAs), hardwareaccelerators, other circuitry, and/or the like. As will therefore beunderstood, the processing element 205 may be configured for aparticular use or configured to execute instructions stored in volatileor non-volatile media or otherwise accessible to the processing element205. As such, whether configured by hardware or computer programproducts, or by a combination thereof, the processing element 205 may becapable of performing steps or operations according to embodiments ofthe present invention when configured accordingly.

In one embodiment, the assessment computing entity 100 may furtherinclude or be in communication with non-volatile media (also referred toas non-volatile storage, memory, memory storage, memory circuitry and/orsimilar terms used herein interchangeably). In one embodiment, thenon-volatile storage or memory may include one or more non-volatilestorage or memory media 210, including but not limited to hard disks,ROM, PROM, EPROM, EEPROM, flash memory, MMCs, SD memory cards, MemorySticks, CBRAM, PRAM, FeRAM, NVRAM, MRAM, RRAM, SONOS, FJG RAM, Millipedememory, racetrack memory, and/or the like. As will be recognized, thenon-volatile storage or memory media may store databases, databaseinstances, database management systems, data, applications, programs,program modules, scripts, source code, object code, byte code, compiledcode, interpreted code, machine code, executable instructions, and/orthe like. The term database, database instance, database managementsystem, and/or similar terms used herein interchangeably may refer to acollection of records or data that is stored in a computer-readablestorage medium using one or more database models, such as a hierarchicaldatabase model, network model, relational model, entity-relationshipmodel, object model, document model, semantic model, graph model, and/orthe like.

In one embodiment, the assessment computing entity 100 may furtherinclude or be in communication with volatile media (also referred to asvolatile storage, memory, memory storage, memory circuitry and/orsimilar terms used herein interchangeably). In one embodiment, thevolatile storage or memory may also include one or more volatile storageor memory media 215, including but not limited to RAM, DRAM, SRAM, FPMDRAM, EDO DRAM, SDRAM, DDR SDRAM, DDR2 SDRAM, DDR3 SDRAM, RDRAM, TTRAM,T-RAM, Z-RAM, RIMM, DIMM, SIMM, VRAM, cache memory, register memory,and/or the like. As will be recognized, the volatile storage or memorymedia may be used to store at least portions of the databases, databaseinstances, database management systems, data, applications, programs,program modules, scripts, source code, object code, byte code, compiledcode, interpreted code, machine code, executable instructions, and/orthe like being executed by, for example, the processing element 205.Thus, the databases, database instances, database management systems,data, applications, programs, program modules, scripts, source code,object code, byte code, compiled code, interpreted code, machine code,executable instructions, and/or the like may be used to control certainaspects of the operation of the assessment computing entity 100 with theassistance of the processing element 205 and operating system.

As indicated, in one embodiment, the assessment computing entity 100 mayalso include one or more communications interfaces 220 for communicatingwith various computing entities, such as by communicating data, content,information, and/or similar terms used herein interchangeably that canbe transmitted, received, operated on, processed, displayed, stored,and/or the like. Such communication may be executed using a wired datatransmission protocol, such as fiber distributed data interface (FDDI),digital subscriber line (DSL), Ethernet, asynchronous transfer mode(ATM), frame relay, data over cable service interface specification(DOCSIS), or any other wired transmission protocol. Similarly, theassessment computing entity 100 may be configured to communicate viawireless external communication networks using any of a variety ofprotocols, such as general packet radio service (GPRS), Universal MobileTelecommunications System (UMTS), Code Division Multiple Access 2000(CDMA2000), CDMA2000 1× (1×RTT), Wideband Code Division Multiple Access(WCDMA), Time Division-Synchronous Code Division Multiple Access(TD-SCDMA), Long Term Evolution (LTE), Evolved Universal TerrestrialRadio Access Network (E-UTRAN), Evolution-Data Optimized (EVDO), HighSpeed Packet Access (HSPA), High-Speed Downlink Packet Access (HSDPA),IEEE 802.11 (Wi-Fi), Wi-Fi Direct, 802.16 (WiMAX), ultra wideband (UWB),infrared (IR) protocols, near field communication (NFC) protocols,Wibree, Bluetooth protocols, wireless universal serial bus (USB)protocols, and/or any other wireless protocol.

Although not shown, the assessment computing entity 100 may include orbe in communication with one or more input elements, such as a keyboardinput, a mouse input, a touch screen/display input, motion input,movement input, audio input, pointing device input, joystick input,keypad input, and/or the like. The assessment computing entity 100 mayalso include or be in communication with one or more output elements(not shown), such as audio output, video output, screen/display output,motion output, movement output, and/or the like.

In various embodiments, the assessment computing entity 100 may furthercomprise a user interface for user interaction. In various embodiments,the user interface may comprise one or more input devices (e.g., soft orhard keyboard, joystick, mouse, touch screen device, microphone, and/orthe like) for receiving user input and one or more output devices (e.g.,speakers, display, and/or the like) for providing output to a user.

As will be appreciated, one or more of the assessment computing entity's100 components may be located remotely from other assessment computingentity 100 components, such as in a distributed system. Furthermore, oneor more of the components may be combined and additional componentsperforming functions described herein may be included in the assessmentcomputing entity 100. Thus, the assessment computing entity 100 can beadapted to accommodate a variety of needs and circumstances. As will berecognized, these architectures and descriptions are provided forexemplary purposes only and are not limiting to the various embodiments.

2. Exemplary User Computing Entity

A user may be an individual, a family, a company, an organization, anentity, a department within an organization, a representative of anorganization and/or person, and/or the like. In one example, users maybe medical personnel, doctors, physician assistants, nurses, patients,and/or the like. For instance, a user may operate a user computingentity 110 that includes one or more components that are functionallysimilar to those of the assessment computing entity 100. FIG. 3 providesan illustrative schematic representative of a user computing entity 110that can be used in conjunction with embodiments of the presentinvention. In general, the terms device, system, computing entity,entity, and/or similar words used herein interchangeably may refer to,for example, one or more computers, computing entities, desktops, mobilephones, tablets, phablets, notebooks, laptops, distributed systems,wearables, input terminals, servers or server networks, blades,gateways, switches, processing devices, processing entities, set-topboxes, relays, routers, network access points, base stations, the like,and/or any combination of devices or entities adapted to perform thefunctions, operations, and/or processes described herein. User computingentities 110 can be operated by various parties. As shown in FIG. 3, theuser computing entity 110 can include an antenna 312, a transmitter 304(e.g., radio), a receiver 306 (e.g., radio), and a processing element308 (e.g., CPLDs, microprocessors, multi-core processors, coprocessingentities, ASIPs, microcontrollers, and/or controllers) that providessignals to and receives signals from the transmitter 304 and receiver306, respectively.

The signals provided to and received from the transmitter 304 and thereceiver 306, respectively, may include signaling information inaccordance with air interface standards of applicable wireless systems.In this regard, the user computing entity 110 may be capable ofoperating with one or more air interface standards, communicationprotocols, modulation types, and access types. More particularly, theuser computing entity 110 may operate in accordance with any of a numberof wireless communication standards and protocols, such as thosedescribed above with regard to the assessment computing entity 100. In aparticular embodiment, the user computing entity 110 may operate inaccordance with multiple wireless communication standards and protocols,such as UMTS, CDMA2000, 1×RTT, WCDMA, TD-SCDMA, LTE, E-UTRAN, EVDO,HSPA, HSDPA, Wi-Fi, Wi-Fi Direct, WiMAX, UWB, IR, NFC, Bluetooth, USB,and/or the like. Similarly, the user computing entity 110 may operate inaccordance with multiple wired communication standards and protocols,such as those described above with regard to the assessment computingentity 100 via a network interface 320.

Via these communication standards and protocols, the user computingentity 110 can communicate with various other entities using conceptssuch as Unstructured Supplementary Service Data (USSD), Short MessageService (SMS), Multimedia Messaging Service (MMS), Dual-ToneMulti-Frequency Signaling (DTMF), and/or Subscriber Identity ModuleDialer (SIM dialer). The user computing entity 110 can also downloadchanges, add-ons, and updates, for instance, to its firmware, software(e.g., including executable instructions, applications, programmodules), and operating system.

According to one embodiment, the user computing entity 110 may includelocation determining aspects, devices, modules, functionalities, and/orsimilar words used herein interchangeably. For example, the usercomputing entity 110 may include outdoor positioning aspects, such as alocation module adapted to acquire, for example, latitude, longitude,altitude, geocode, course, direction, heading, speed, universal time(UTC), date, and/or various other information/data. In one embodiment,the location module can acquire data, sometimes known as ephemeris data,by identifying the number of satellites in view and the relativepositions of those satellites. The satellites may be a variety ofdifferent satellites, including Low Earth Orbit (LEO) satellite systems,Department of Defense (DOD) satellite systems, the European UnionGalileo positioning systems, the Chinese Compass navigation systems,Indian Regional Navigational satellite systems, and/or the like.Alternatively, the location information can be determined bytriangulating the user computing entity's 110 position in connectionwith a variety of other systems, including cellular towers, Wi-Fi accesspoints, and/or the like. Similarly, the user computing entity 110 mayinclude indoor positioning aspects, such as a location module adapted toacquire, for example, latitude, longitude, altitude, geocode, course,direction, heading, speed, time, date, and/or various otherinformation/data. Some of the indoor systems may use various position orlocation technologies including RFID tags, indoor beacons ortransmitters, Wi-Fi access points, cellular towers, nearby computingdevices (e.g., smartphones, laptops) and/or the like. For instance, suchtechnologies may include the iBeacons, Gimbal proximity beacons,Bluetooth Low Energy (BLE) transmitters, NFC transmitters, and/or thelike. These indoor positioning aspects can be used in a variety ofsettings to determine the location of someone or something to withininches or centimeters.

The user computing entity 110 may also comprise a user interface (thatcan include a display 316 coupled to a processing element 308) and/or auser input interface (coupled to a processing element 308). For example,the user interface may be a user application, browser, user interface,and/or similar words used herein interchangeably executing on and/oraccessible via the user computing entity 110 to interact with and/orcause display of information from the assessment computing entity 100,as described herein. The user input interface can comprise any of anumber of devices or interfaces allowing the user computing entity 110to receive data, such as a keypad 318 (hard or soft), a touch display,voice/speech or motion interfaces, or other input device. In embodimentsincluding a keypad 318, the keypad 318 can include (or cause display of)the conventional numeric (0-9) and related keys (#, *), and other keysused for operating the user computing entity 110 and may include a fullset of alphabetic keys or set of keys that may be activated to provide afull set of alphanumeric keys. In addition to providing input, the userinput interface can be used, for example, to activate or deactivatecertain functions, such as screen savers and/or sleep modes.

The user computing entity 110 can also include volatile storage ormemory 322 and/or non-volatile storage or memory 324, which can beembedded and/or may be removable. For example, the non-volatile memorymay be ROM, PROM, EPROM, EEPROM, flash memory, MMCs, SD memory cards,Memory Sticks, CBRAM, PRAM, FeRAM, NVRAM, MRAM, RRAM, SONOS, FJG RAM,Millipede memory, racetrack memory, and/or the like. The volatile memorymay be RAM, DRAM, SRAM, FPM DRAM, EDO DRAM, SDRAM, DDR SDRAM, DDR2SDRAM, DDR3 SDRAM, RDRAM, TTRAM, T-RAM, Z-RAM, RIMM, DIMM, SIMM, VRAM,cache memory, register memory, and/or the like. The volatile andnon-volatile storage or memory can store databases, database instances,database management systems, data, applications, programs, programmodules, scripts, source code, object code, byte code, compiled code,interpreted code, machine code, executable instructions, and/or the liketo implement the functions of the user computing entity 110. Asindicated, this may include a user application that is resident on theentity or accessible through a browser or other user interface forcommunicating with the assessment computing entity 100 and/or variousother computing entities.

In another embodiment, the user computing entity 110 may include one ormore components or functionality that are the same or similar to thoseof the assessment computing entity 100, as described in greater detailabove. As will be recognized, these architectures and descriptions areprovided for exemplary purposes only and are not limiting to the variousembodiments.

3. Exemplary Imaging Machine

In various embodiments, an imaging machine 115 may be an imaging machineconfigured to capture and/or generate imaging information/data used tocreate a 2D, 3D, or 4D image of a portion of an individual's (e.g.,patient, study participant, and/or the like) body. In variousembodiments, the imaging machine 115 is configured for capturing and/orgenerating imaging information/data through at least one of the variousimaging techniques and/or processes such as, for example, fluoroscopy,magnetic resonance imaging (MRI), photoacoustic imaging, positronemission tomography (PET), projection radiography, computed axialtomography (CT scan), and ultrasound. In an example embodiment, theimaging machine 115 is an MRI machine configured for capturing and/orgenerating image information/data of an individual's (e.g., patient,study participant, and/or the like) brain. In an example embodiment, theimaging machine 115 is an MRI machine configured for dMRI imaging.

In an example embodiment, the imaging machine 115 may be operated by theassessment computing entity 100 and/or other controller computingentity. In an example embodiment, the imaging machine and/or controllercomputing entity thereof may communicate with the assessment computingentity 100 directly and/or via one or more wired and/or wirelessnetworks 105. For example, the imaging machine 115 may be configured toprovide imaging information/data to the assessment computing entity 100for analysis, storage, and/or the like. In another example, theassessment computing entity 100 and/or other controller computing entitymay provide instructions to the imaging machine 115 regarding beginningan imaging session, pausing an imaging session, aborting an imagingsession, ending an imaging session, and/or the like.

III. EXEMPLARY SYSTEM OPERATION

According to various embodiments, the assessment computing entity 100may be configured to analyze one or more instances of imaginginformation/data. In an example embodiment, an instance of imaginginformation/data may comprise the imaging information/data of oneimaging session and/or a portion of one imaging session, for oneindividual (e.g., patient, study participant, and/or the like). The term“image” is used generically to refer to a variety of images that can begenerated from various imaging techniques and processes. The imagingtechniques and processes may include, for instance, diffusion MRI. Asindicated, the images can be of a human body or one or more parts of thehuman body (e.g., a patient and/or study participant's brain), but theimages can also be of other organisms or objects. A “volume of images”or “volume” refers to a sequence of images that can be spatially relatedand assembled into a rectilinear block representing a dimensional regionof an individual's (e.g., patient, study participant, and/or the like)anatomy. In various embodiments, the imaging information/data capturedand analyzed comprises dMRI data. Although the following is described inthe context of MRI scans, embodiments of the present invention are notlimited to this context.

In an example embodiment, one or more instances of imaginginformation/data may be captured and/or generated for an individual(e.g., patient, study participant, and/or the like), wherein an instanceof imaging information/data may comprise a dMRI image. For example, dMRIimaging information/data may be analyzed using a bi-tensor analysis todetermine the amount and/or fraction of free-water present within one ormore areas of an individual's (e.g., patient, study participant, and/orthe like) brain. In an example embodiment, two or more instances ofimaging data/information, and/or the analysis thereof, corresponding tothe same individual (e.g., patient, study participant, and/or the like)may be compared, analyzed as a set, and/or the like. For example, thetwo or more instances of imaging data/information may be captured and/orgenerated at different times (e.g., separated by a week, a month, twomonths, six months, a year, two years, four years, and/or the like).Such analysis may be used to determine a progression or lack ofprogression of a disease, such as Parkinson's or other parkinsoniandisease; measure the efficacy of a drug and/or other therapy attreating, slowing, and/or halting the progression of a disease, such asParkinson's or other parkinsonian disease; determine a risk level,score, and/or the like for the individual (e.g., patient, studyparticipant, and/or the like) of developing a disease or condition, suchas Parkinson's or other parkinsonian disease; diagnose early stages of adisease, such as Parkinson's or other parkinsonian disease, before theprimary diagnostic symptoms (e.g., movement and/or motor symptoms)become evident; and/or the like. For example, various embodiments may beprovide for determining a parkinsonian state for an individual.Additionally, various embodiments are configured to identify a treatmentfor an individual for treating a parkinsonian disease. In variousembodiments, the imaging information/data is analyzed using an automatednormalization pipeline that is configured to automatically identify oneor more regions of the individual's brain within the imaginginformation/data.

Determining an Individual's Parkinsonian State

FIG. 4 provides a flowchart of various processes, procedures,operations, and/or the like that may be completed to determine anindividual's parkinsonian state, identify a free-water pattern for anindividual's brain, and/or the like. In various embodiments, theindividual may be an individual patient. In various embodiments, theindividual may be a study participant for a drug study or othertreatment study.

Starting at block 402, imaging information/data is captured. Forexample, the assessment computing entity 100 may receive from or causean imaging device 115 to capture imaging information/data of anindividual. For example, an imaging technician may provide theindividual with instructions and position the individual within and/orin the proximity of the imaging device 115. The imaging technician maythen provide input (e.g., via a user input device) to the assessmentcomputing entity 100 to trigger the assessment computing entity 100 tocause, instruct, trigger, and/or the like the imaging device 115 tocapture imaging information/data of the individual. The assessmentcomputing entity 100 may receive the captured imaging information/datafrom the imaging device 115 and store the imaging information/data andcorresponding metadata in memory 210, 215. For example, the metadata mayinclude the individual's name or an individual identifier, a date and/ortime stamp, information/data identifying the imaging device 115, and/orthe like. In an example embodiment, the imaging information/datacaptured may comprise dMRI data and/or the like. In various embodiments,the imaging information/data may comprise one or more images of anindividual's brain and/or a portion thereof.

At block 404, one or more portions of imaging information/datacorresponding to one or more areas of the brain are identified. Forexample, the one or more areas of the brain that are identified may bereferred to as regions of interest. In an example embodiment, theregions of interest may comprise one or more of the anterior substantianigra, posterior substantia nigra, putamen, caudate nucleus, globuspallidus, subthalamic nucleus, middle cerebellar peduncle, superiorcerebellar peduncle, cerebellar lobule VI, inferior vermis,pedunculopontine nucleus, hippocampus, and/or the like. For example, theassessment computing entity 100 may receive user input (e.g., via aninput device) that identify portions of imaging information/datacorresponding to regions of interest. For example, the assessmentcomputing entity 100 may display one or more images corresponding to theimaging information/data to a skilled technician via an output device(e.g., display of the assessment computing entity 100, display 316 of auser computing entity 110, and/or the like). The assessment computingentity 100 may then receive an indication of user input (e.g., receivedvia an input device of the assessment computing entity 100 or of theuser computing entity 110) identifying one or more portions of the oneor more images that correspond to particular areas of the individual'sbrain. For example, the skilled technician may provide input to theinput device of the assessment computing entity 100 or user computingentity 110 identifying

In an example embodiment, the portions of imaging information/datacorresponding to the regions of interest are identified by theassessment computing entity 100. For example, the assessment computingentity 100 may use a trained neural network, deep net, or other modelthat was trained using machine learning to identify one or more portionsof the imaging information/data corresponding to one or more areas ofthe brain. For example, the assessment computing entity 100 may use anormalization pipeline, one or more templates, and/or the like foridentifying one or more portions of the imaging information/datacorresponding to one or more areas of the brain such as the regions ofinterest.

In various embodiments, the imaging information/data may bepre-processed before being provided (e.g., displayed) for skilledtechnician identification of the regions of interest and/or automatedidentification of the regions of interest. For example, in an exampleembodiment, the imaging information/data may be preprocessed using FMRIBSoftware Library (FSL, http://www.fmrib.ox.ac.uk/fsl/) and custom UNIXshell scripts. For example, each scan of the imaging information/datawas corrected for signal distortions due to eddy currents and headmotion. For example, gradient directions may be rotated in response tothe eddy current corrections, and portions of the imaginginformation/data corresponding to non-brain tissue may be removed fromthe imaging information/data. In example embodiment, we use customalgorithms to quantify b0 and corrected fractional anisotropy imagesfrom the imaging information/data. In various embodiments, the b0 imageis the diffusion image without gradients applied with diffusionweighting and is similar to a standard T2 weighted MRI. Free-waterimages and free-water-corrected diffusion tensor images may becalculated using custom algorithms in MATLAB or another mathematicalanalysis software based on the imaging information/data. In an exampleembodiment, a bi-tensor model is used to calculate the signalattenuation as the sum of attenuations arising from two compartments:one that models free-water and a tissue compartment. Thefree-water-corrected tensor images were also used to calculatefree-water corrected fraction anisotropy maps (FA_(T)). Datapreprocessing may be used to generate fractional anisotropy, b0,free-water, FA_(T) and/or other images from and/or based on the imagingdata, in various embodiments. In an example embodiment, the fractionalanisotropy and b0 images were multiplied together to maximize contrastin the midbrain and the cortex, and will hereafter be referred to as theFA*b0 image. In an example embodiment, the portions of imaginginformation/data corresponding to the regions of interest are identifiedin the b0 image in subject space. For example, an image provided forskilled technician identification of one or more regions of interest maybe a b0 image in subject space.

In some example embodiments, FLIRT/FNIRT in FSL software and/or rigidalignment and Symmetric Normalization (SyN) in Advanced NormalizationTools (ANTs) software may be used to provide a normalization pipelinefor processing imaging information/data and/or identifying one or moreportions of imaging information/data corresponding to regions ofinterest. In some example embodiments, FLIRT/FNIRT in FSL softwareand/or rigid alignment and Symmetric Normalization (SyN) in AdvancedNormalization Tools (ANTs) software may be used to generate one or moretemplates used to identify one or more portions of the imaginginformation/data corresponding to one or more regions of interest and/oruse one more templates to identify portions of the imaginginformation/data corresponding to regions of interest. In an exampleembodiment, at least one of the templates corresponds to and/or isgenerated based on mean b0, fractional anisotropy, or b0*FA images. Inan example embodiment, the imaging information/data is analyzed and/orthe templates are in Montreal Neurological Institute (MNI) space.Processing through this pipeline is an automated computer drivenprocedure that can than link to cloud based architecture or laptopscomputing devices, or the like (e.g., a user computing entity 110).

In various embodiments, to transform the subject space imagine data(e.g., the imaging information/data received from the imaging device 115and/or the pre-processed imaging data) to MNI space, a series ofnonlinear and linear registrations may be used. In various embodiments,prior to nonlinear registration, a linear registration (e.g., FLIRT inFSL and a rigid transformation in ANTs) may be applied to the imaginginformation/data. In an example embodiment, three linear registrationsmay be completed in FSL, ANTs, and/or the like to get subject-spaceimages to MNI space. In an example embodiment, the three linearregistrations comprise (1) b0 registration to a mean 100 HumanConnectome Project (HCP) b0 template, (2) fractional anisotropyregistration to a mean HCP fractional anisotropy template, and (3) FA*b0registration to a mean HCP FA*b0 template. Linear registrations werefollowed with nonlinear warping (e.g., FNIRT in FSL and SyN in ANTs). Inan example embodiment, three different nonlinear warps may be performedfor FSL, ANTs, and/or the like. For example, the three differentnonlinear warps may comprise (1) b0 warping to a mean HCP b0 template,(2) fractional anisotropy warping to a mean HCP fractional anisotropytemplate, and (3) FA*b0 warping to a mean HCP FA*b0 template. In anexample embodiment, default settings may be used for all registrationand warping.

At block 406, a free-water fraction is determined for one or more areasof the brain based on the imaging information/data. For example, thefree-water fraction for one or more regions of interest may bedetermined based on the portions of imaging information/datacorresponding to the respective regions of interest. For example, theassessment computing entity 100 may determine the free-water fractionfor a region of interest based on the portion of the imaginginformation/data identified as corresponding to the region of interest.In various embodiments, the free-water fraction is derived from imaginginformation/data captured via dMRI scan. Various methods for derivingthe free-water fraction for a region of interest may be used in variousembodiments.

At block 408, a free-water pattern for the individual may be determined.In an example embodiment, the free-water pattern may comprise thefree-water fraction determined for each of a one or more regions ofinterest. For instance, the assessment computing entity 100 maydetermine a free-water pattern for the individual based on the imaginginformation/data. In an example embodiment, the free-water pattern maybe a list of one or more regions of interest and/or other areas of theindividuals brain and the determined free-water fraction correspondingto the one or more regions of interest and/or other areas of theindividual's brain, a relative measure of the free-water fractioncorresponding to the one or more regions of interest and/or other areasof the individual's brain (e.g., relative to one or more other regionsof interest and/or other areas of the individual's brain, relative to anormal brain template, and/or the like), and/or the like. In an exampleembodiment, the free-water pattern may be an image or brain mapindicating the determined free-water fraction corresponding to the oneor more regions of interest and/or other areas of the individual'sbrain, a relative measure of the free-water fraction corresponding tothe one or more regions of interest and/or other areas of theindividual's brain (e.g., relative to one or more other regions ofinterest and/or other areas of the individual's brain, relative to anormal brain template, and/or the like), and/or the like. In variousembodiments, the free-water pattern may be an absolute or relativedescription of the free-water fraction in two or more regions ofinterest and/or other areas of the individual's brain.

At block 410, the free-water pattern may be used to determine theindividual's parkinsonian status. For example, the assessment computingentity 100 may determine the individual's parkinsonian status based onthe free-water pattern. In various embodiments, the free-water patternmay be used to determine if the individual has a parkinsonian disease,if the individual is at risk of developing a parkinsonian disease,and/or the like. Further, the free-water pattern may be used todetermine a type of parkinsonian disease (e.g., Parkinson's disease(PD), multiple system atrophy (MSA), and progressive supranuclear palsy(PSP), atypical parkinsonism, cortical basal syndrome, and/or the like)that the individual has or is at risk of developing. In variousembodiments, the individual's parkinsonian state may be determined bymatching the individual's free-water pattern to a most similarfree-water pattern template, by determining if the individual'sfree-water pattern satisfies one or more one-dimensional,two-dimensional, or three-dimensional predetermined thresholdrequirements, and/or the like. In various embodiments, the free-waterpattern templates and/or predetermined threshold requirements may beempirically derived, determined, and/or the like. In variousembodiments, an individual's parkinsonians state may be that theindividual does not and/or is unlikely to have a parkinsonian disease,is at risk of developing a parkinsonian disease, has an early stageparkinsonian disease, has a middle stage parkinsonian disease, has alate stage parkinsonian disease, has a particular parkinsonian disease(e.g., PD, MSA, PSP, atypical parkinsonism, cortical basal syndrome,and/or the like), and/or the like.

After determining the individual's parkinsonian state, the individual'sparkinsonian state may be provided to a skilled technician (e.g.,laboratory technician, physician, physician assistant, nurse, and/or thelike). For example, the assessment computing entity 100 may provide thedetermined individual's state via an output device (e.g., display),communications interface 220, and/or the like. For example, the usercomputing entity 110 may receive a message comprising the determinedindividual's parkinsonian state and provide the individual'sparkinsonian state via display 316, and/or other output device of theuser computing entity 110.

In various embodiments, the determination of an individual'sparkinsonian state may be used for research (e.g., in a medical researchstudy, a drug or other treatment study, and/or the like), to determine acourse of treatment for the individual (e.g., a set of one or moreprescriptions, physical therapy, occupational therapy, and/or othertherapy), and/or the like. In an example embodiment, the assessmentcomputing entity 100 may provide a suggested course of treatment for theindividual. In various embodiments, the course of treatment may bedetermined by one or more physicians and/or other healthcare providers.

Determining a Treatment Efficacy

FIG. 5 provides a flowchart illustrating various processes, procedures,operations, and/or the like for determining a disease progression score,determining a treatment efficacy, determining a course of treatment,and/or the like for an individual having and/or at risk of developingparkinsonian disease.

Starting at block 502, a first set of imaging information/data iscaptured at a first date and/or time. For example, the assessmentcomputing entity 100 may cause an imaging device 115 to capture a firstset of imaging information/data of an individual. For example, animaging technician may provide the individual with instructions andposition the individual within and/or in the proximity of the imagingdevice 115. The imaging technician may then provide input (e.g., via auser input device) to the assessment computing entity 100 to trigger theassessment computing entity 100 to cause, instruct, trigger, and/or thelike the imaging device 115 to capture the first set of imaginginformation/data of the individual. The assessment computing entity 100may receive the captured first set of imaging information/data from theimaging device 115 and store the first set of imaging information/dataand corresponding metadata in memory 210, 215. For example, the metadatamay include the individual's name or an individual identifier, a dateand/or time stamp indicating a first date and/or time at which first setof imaging information/data was captured, information/data identifyingthe imaging device 115, and/or the like. In an example embodiment, thefirst set of imaging information/data captured may comprise dMRI data,and/or the like. In various embodiments, the first set of imaginginformation/data may comprise one or more images of an individual'sbrain and/or a portion thereof.

At block 504, a first free-water pattern is determined for theindividual at the first date and/or time. In various embodiments, thefirst set of imaging data is pre-processed to remove any motionartifacts and/or distortions prior to the determining of the firstfree-water pattern. In an example embodiment, the pre-processing of thefirst set of imaging data may be performed via an automated pipeline(e.g., performed by the assessment computing entity 100, and/or thelike). For example, the assessment computing entity 100 may determine afirst free-water pattern for the individual at the first date and/ortime. For example, a technique similar to that described for block404-408 above may be used to determine a first free-water pattern basedon the first set of imaging information/data. For example, one or moreportions of the first set of imaging information/data corresponding toone or more areas of the individual's brain may be identified. Forexample, one or more portions of the first set of imaginginformation/data corresponding to regions of interest may be identified.Free-water fractions for one or more regions of interest and/or otherareas of the individual's brain may be determined based on thecorresponding portions of the first set of imaging information/data. Thedetermined free-water fractions and/or the first set of imaginginformation/data may then be used to derive and/or determine the firstfree-water pattern for the individual at the first date and/or time.

Starting at block 506, a second set of imaging information/data iscaptured at a second date and/or time. In various embodiments the secondset of imaging information/data is captured at a second date and/or timethat is after the first date and/or time. In an example embodiment, thesecond set of imaging information/data is captured at a second dateand/or time that is at least a minimum time window after the first dateand/or time. In various embodiments, the minimum time window may be aweek, a month, a three months, half a year, one year, more than oneyear, and/or the like. In an example embodiment, the minimum time windowmay be predetermined and/or may be determined based on the firstfree-water pattern.

In an example embodiment, the assessment computing entity 100 may causean imaging device 115 to capture a second set of imaginginformation/data of an individual at the second date and/or time. Forexample, an imaging technician may provide the individual withinstructions and position the individual within and/or in the proximityof the imaging device 115. The imaging technician may then provide input(e.g., via a user input device) to the assessment computing entity 100to trigger the assessment computing entity 100 to cause, instruct,trigger, and/or the like the imaging device 115 to capture the secondset of imaging information/data of the individual. The assessmentcomputing entity 100 may receive the captured second set of imaginginformation/data from the imaging device 115 and store the second set ofimaging information/data and corresponding metadata in memory 210, 215.For example, the metadata may include the individual's name or anindividual identifier, a date and/or time stamp indicating a second dateand/or time at which second set of imaging information/data wascaptured, information/data identifying the imaging device 115, and/orthe like. In an example embodiment, the second set of imaginginformation/data captured may comprise dMRI data, and/or the like. Invarious embodiments, the second set of imaging information/data maycomprise one or more images of an individual's brain and/or a portionthereof.

At block 508, a second free-water pattern is determined for theindividual at the second date and/or time. In various embodiments, thesecond set of imaging data is pre-processed to remove any motionartifacts and/or distortions prior to the determining of the secondfree-water pattern. In an example embodiment, the pre-processing of thesecond set of imaging data may be performed via an automated pipeline(e.g., performed by the assessment computing entity 100, and/or thelike). For example, the assessment computing entity 100 may determine asecond free-water pattern for the individual at the first date and/ortime. For example, a technique similar to that described for blocks404-408 above may be used to determine a second free-water pattern basedon the second set of imaging information/data. For example, one or moreportions of the second set of imaging information/data corresponding toone or more areas of the individual's brain may be identified. Forexample, one or more portions of the second set of imaginginformation/data corresponding to regions of interest may be identified.Free-water fractions for one or more regions of interest and/or otherareas of the individual's brain may be determined based on thecorresponding portions of the second set of imaging information/data.The determined free-water fractions and/or the second set of imaginginformation/data may then be used to derive and/or determine the secondfree-water pattern for the individual at the second date and/or time.

At block 510, a progression score is determined. For example, theassessment computing entity 100 may determine a progression score forthe individual based on the first and second free-water pattern. Forexample, the progression score may be determined by fitting a firsttemplate to the first free-water pattern and/or identifying one or morefirst threshold requirements satisfied by the first free-water patternand fitting a second template to the second free-water pattern and/oridentifying one or more second threshold requirements satisfied by thesecond free-water pattern. The first and second templates and/or firstand second threshold requirements may each be associated with a diseasestage and the differences between the disease stages associated with thefirst and second templates and/or the first and second thresholdrequirements may be taken as the progression score. In another exampleembodiment, the differences between the first free-water pattern and thesecond free-water pattern may be determined to generate a differencefree-water pattern. The difference free-water pattern may then be fit toa difference template and/or one or more difference thresholdrequirements satisfied by the difference free-water pattern may beidentified. The difference template and/or difference thresholdrequirements may each be associated with a disease progression. Thus,progression score may be determined based on the fitted differencetemplate and/or identified one or more difference thresholdrequirements. In various embodiments, the first, second, and/ordifference threshold requirements may be one-dimensional,two-dimensional, or three-dimensional threshold requirements.

In an example embodiment, the assessment computing entity 100 may storein memory 210, 215 and/or access a sequence of templates, thresholdrequirements, difference templates, and/or difference thresholdrequirements to determine which template, threshold requirements,difference template, and/or difference threshold requirements providethe best fit and/or are satisfied by the first free-water pattern,second free-water pattern, and/or difference free-water pattern. Asshown by the inventor, changes in the free-water pattern of anindividual is not linear with disease progression for parkinsoniandiseases. In particular, the free-water pattern of an individual changesmore rapidly at earlier stages of the disease than at later stages ofthe disease. Thus, the sequence of templates, threshold requirements,difference templates, and/or difference threshold requirements mayinclude a larger number of templates, threshold requirements, differencetemplates, and/or difference threshold requirements corresponding toearlier stages of one or more parkinsonian diseases.

In another example embodiment, a first parkinsonian state is determinedbased on the first free-water pattern and a second parkinsonian state isdetermined based on the second free-water pattern. The progression scoremay be determined based on the number of states and/or distance betweenthe first parkinsonian state and the second parkinsonian state. Inanother example embodiment, the progression score may provide alikelihood that the individual having the first parkinsonian state atthe first date and/or time would have the second parkinsonian state andthe second date and/or time based on the natural progression of theparkinsonian disease.

At block 512, a treatment efficacy score is determined. For example, theassessment computing entity 100 may determine a treatment efficacyscore. As described above, a treatment comprise one or more drugs,pharmaceuticals, and/or prescriptions, physical therapy, occupationaltherapy, and/or the like. For example, the treatment efficacy score mayprovide an indication of how well a treatment or treatment plan hasprevented and/or reversed the natural progression of the parkinsoniandisease. In an example embodiment, the treatment efficacy score isdetermined based on the determined progression score, the amount of timeand/or time window between the first date and/or time and the seconddate and/or time, the portion of the amount of time between the firstdate and/or time and the second date and/or time for which theindividual has been undergoing the treatment, and/or the like. Forexample, if the natural progression of the parkinsonian disease of anaverage individual having a first parkinsonian state at the first timeis a third parkinsonian state at the second date and/or time, and it isdetermined that the individual had a first parkinsonian state at thefirst date and/or time and a second parkinsonian state at the seconddate and/or time, the second parkinsonian state being intermediatebetween the first parkinsonian state and the third parkinsonian state,the treatment efficacy score may indicate a “distance” between thesecond parkinsonian state and the third parkinsonian state. The“distance” may be a time difference (e.g., the amount of time between anaverage individual usually has the second parkinsonian state and theaverage individual has the third parkinsonian state in accordance withnatural disease progression), a rate difference (e.g., a difference inthe rate of progression of the disease with respect to natural diseaseprogression in an average individual), a percent difference (e.g., apercentage difference between the expected third parkinsonian state andthe determined second parkinsonian state), and/or the like.

In various embodiments, the progression score, treatment efficacy score,and/or a second parkinsonian state determined for the individual basedon the second free-water pattern may be provided. For example, theindividual's progression score, treatment efficacy score, and/or secondparkinsonian state may be provided to a skilled technician (e.g.,laboratory technician, physician, physician assistant, nurse, and/or thelike). For example, the assessment computing entity 100 may provide thedetermined individual's progression score, treatment efficacy score,and/or second parkinsonian state via an output device (e.g., display),communications interface 220, and/or the like. For example, the usercomputing entity 110 may receive a message comprising the determinedindividual's progression score, treatment efficacy score, and/or secondparkinsonian state and provide the individual's parkinsonian state viadisplay 316, and/or other output device of the user computing entity110. In various embodiments, the individual's progression score,treatment efficacy score, second parkinsonian state, and/or the like maybe stored in a treatment and/or drug study database stored in memory210, 215, and/or the like.

At block 514, a treatment plan may be determined for the individual. Forexample, the treatment plan may be determined based on the secondparkinsonian state, the treatment efficacy score, progression score,and/or the like. In an example embodiment, the assessment computingentity 100 may provide a suggested course of treatment for theindividual. In various embodiments, the course of treatment may bedetermined by one or more physicians and/or other healthcare providers.In various embodiments, the determination of an individual's progressionscore, treatment efficacy score, and/or second parkinsonian state may beused for research (e.g., in a medical research study such as, forexample, a medical research study that includes evaluation of one ormore pharmaceutical drug trial or other treatment and/or therapy).

Advantages

Various embodiments of the present invention provide significanttechnical advantages and address technical challenges of treatingparkinsonian disease, evaluating treatment efficacy in treatingparkinsonian disease, and/or the like. For example, various embodimentsprovide for a normalized, repeatable, and consistent manner foridentifying various areas of an individual's brain from imaging data fordetermining a free-water pattern for an individual's brain. The currentprocedures provide a brain derived template, pipeline for registering tothe template, procedures for quantifying free-water and other diffusionpatterns, and automated procedures for assessing parkinsonian state.Additionally, various embodiments of the present invention provide fortreatment of parkinsonian disease using a non-invasive biomarker.Various embodiments of the present invention provide a treatmentefficacy score that may be used to determine the efficacy of a treatmentsuch that the treatment may be evaluated. For example, the evaluationmay be for an individual or for a treatment and/or drug study.

IV. CONCLUSION

Many modifications and other embodiments of the inventions set forthherein will come to mind to one skilled in the art to which theseinventions pertain having the benefit of the teachings presented in theforegoing descriptions and the associated drawings. Therefore, it is tobe understood that the inventions are not to be limited to the specificembodiments disclosed and that modifications and other embodiments areintended to be included within the scope of the appended claims.Although specific terms are employed herein, they are used in a genericand descriptive sense only and not for purposes of limitation.

The invention claimed is:
 1. A method for determining a free-waterpattern of an individual, the method comprising: receiving, at acomputing entity, an instance of imaging information/data associatedwith a dMRI scan of an individual's brain; and using, by the computingentity, at least one linear registration followed by at least onenon-linear warping and at least one of (a) a free-water pattern templateor (b) 2D or 3D threshold requirements to determine a free-water patternfor one or more areas of the individual's brain based at least in parton a portion of the instance of imaging data, wherein the free-waterpattern is used to determine the individual's parkinsonian state, andwherein the at least one linear registration comprises: b0 registrationto a mean b0 template; fractional anisotropy (FA) registration to a meanFA template; and FA*b0 registration to a mean FA*b0 template.
 2. Themethod of claim 1, wherein the free-water pattern comprises a free-waterfraction of the one or more areas of the individual's brain and the oneor more areas of the individual's brain comprise one or more of theanterior substantia nigra, posterior substantia nigra, putamen, caudatenucleus, globus pallidus, subthalamic nucleus, middle cerebellarpeduncle, superior cerebellar peduncle, cerebellar lobule VI, inferiorvermis, pedunculopontine nucleus, or hippocampus.
 3. The method of claim1, wherein a type of parkinsonian disease is determined based at leastin part on the free-water pattern.
 4. The method of claim 1, wherein theone or more areas of the individual's brain are automatically identifiedby the computing entity.
 5. The method of claim 1, wherein at least oneof the (a) free-water pattern template or (b) 2D or 3D thresholdrequirements is generated using an automated normalization pipeline. 6.An apparatus comprising at least one processor, a communicationsinterface configured for communicating via at least one network, and atleast one memory storing computer program code, the at least one memoryand the computer program code configured to, with the processor, causethe apparatus to at least: receive an instance of imaginginformation/data associated with a dMRI scan of an individual's brain;and use at least one linear registration followed by at least onenon-linear registration and at least one of (a) a free-water patterntemplate or (b) 2D or 3D threshold requirements to determine afree-water pattern for one or more areas of the individual's brain basedat least in part on a portion of the instance of imaging data, whereinthe free-water pattern is used to determine the individual'sparkinsonian state, and wherein the at least one linear registrationcomprises: b0 registration to a mean b0 template; fractional anisotropy(FA) registration to a mean FA template; and FA*b0 registration to amean FA*b0 template.
 7. The apparatus of claim 6, wherein the free-waterpattern comprises a free-water fraction of the one or more areas of theindividual's brain and the one or more areas of the individual's braincomprise one or more of the anterior substantia nigra, posteriorsubstantia nigra, putamen, caudate nucleus, globus pallidus, subthalamicnucleus, middle cerebellar peduncle, superior cerebellar peduncle,cerebellar lobule VI, inferior vermis, pedunculopontine nucleus, orhippocampus.
 8. The apparatus of claim 6, wherein a type of parkinsoniandisease is determined based at least in part on the free-water pattern.9. The apparatus of claim 6, wherein the one or more areas of theindividual's brain are automatically identified by the computing entity.10. The apparatus of claim 6, wherein at least one of the (a) free-waterpattern template or (b) 2D or 3D threshold requirements is generatedusing an automated normalization pipeline.
 11. A computer programproduct comprising at least one non-transitory computer-readable storagemedium having computer-readable program code portions stored therein,the computer-readable program code portions comprising executableportions configured, when executed by a processor of an apparatus, tocause the apparatus to: receive an instance of imaging information/dataassociated with a dMRI scan of an individual's brain; and use at leastone linear registration followed by at least one non-linear registrationand at least one of (a) a free-water pattern template or (b) 2D or 3Dthreshold requirements to determine a free-water pattern for one or moreareas of the individual's brain based at least in part on a portion ofthe instance of imaging data, wherein the free-water pattern is used todetermine the individual's parkinsonian state, and wherein the at leastone linear registration comprises: b0 registration to a mean b0template; fractional anisotropy (FA) registration to a mean FA template;and FA*b0 registration to a mean FA*b0 template.
 12. The computerprogram product of claim 11, wherein the free-water pattern comprises afree-water fraction of the one or more areas of the individual's brainand the one or more areas of the individual's brain comprise one or moreof the anterior substantia nigra, posterior substantia nigra, putamen,caudate nucleus, globus pallidus, subthalamic nucleus, middle cerebellarpeduncle, superior cerebellar peduncle, cerebellar lobule VI, inferiorvermis, or pedunculopontine nucleus, hippocampus.
 13. The computerprogram product of claim 11, wherein a type of parkinsonian disease isdetermined based at least in part on the free-water pattern.
 14. Thecomputer program product of claim 11, wherein the one or more areas ofthe individual's brain are automatically identified by the computingentity.
 15. The computer program product of claim 11, wherein at leastone of the (a) free-water pattern template or (b) 2D or 3D thresholdrequirements is generated using an automated normalization pipeline.